Data Engineer I, DSP Analytics

Do you enjoy diving deep into data, developing real-time and batch pipelines that generate actionable insights?
The DSP Analytics team has an exciting opportunity for a Data Engineer to make impactful contributions to Amazon's Delivery Service Partner (DSP) program tackling modern data challenges by combining traditional engineering practices with transformative analytics and Generative AI.

You'll help design and build the next generation of data infrastructure powering GenAI applications, and develop agents that automate the end-to-end data operations lifecycle. We are a team of Data Engineers working closely with Data Scientists, Economists, and Analysts turning machine learning and AI research into scalable products that delight customers worldwide.

You're passionate about technology, strongly biased toward going deep to find insights, and driven to build scalable analytical platforms. You're relentless about quality and reliability, and comfortable communicating across different levels of leadership. If that sounds like you, we'd love to talk.

Key job responsibilities
• Design, build, and maintain robust data pipelines to acquire, process, and store data from various sources such as databases, APIs, and external data providers.
• Develop and optimize ETL (Extract, Transform, Load) processes to clean, enrich, and structure raw data into a usable format for analysis and reporting.
• Implement and manage data warehousing solutions to ensure efficient data storage, retrieval, and query performance.
• Establish data quality standards, perform data validation, and proactively identify and address data quality issues.
• Optimize data pipelines and storage solutions to handle large volumes of data while maintaining high performance and reliability.
• Ensure data privacy and security by implementing access controls, encryption, and compliance with data protection regulations.
• Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and provide the necessary data infrastructure to support their needs.
• Maintain comprehensive documentation for data pipelines, data models, and processes to facilitate knowledge sharing and troubleshooting.
• Implement monitoring solutions to proactively detect and address data pipeline failures or performance bottlenecks.
• Keep abreast of industry trends and emerging technologies in data engineering to recommend and implement improvements to our data infrastructure.

About the team
We are the Amazon DSP Analytics team, supporting all business pillars across the DSP organization horizontally, with the vision to enable data-, insights-, and science-driven decision-making. We are an exceptionally talented and fun-loving team. Here, you'll have the opportunity to dive deep into complex business and data problems, drive large-scale technical solutions, and raise the bar for operational excellence. We love sharing ideas and learning from each other, and we believe in using those ideas to disrupt the status quo.

Basic qualifications

- 1+ years of data engineering experience
- Experience with SQL
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
- Experience with one or more scripting language (e.g., Python, KornShell)
- Knowledge of database, data warehouse, or data lake solutions

Preferred qualifications

- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Knowledge of basics of designing and implementing a data schema like normalization, relational model vs dimensional model
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Demonstrated experience leveraging generative AI tools to enhance workflow efficiency and productivity, with the ability to craft effective prompts and critically evaluate AI-generated outputs in a professional setting
- Experience identifying opportunities to integrate AI solutions into products and services to drive business value

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